What drives the economic performance of suppliers in global value chains/global production networks – tier, ownership, size, specialization, or region?

Figures & data

Table 1. The relationships between GPN casual drivers, modes of strategic coupling, and other key characteristics of companies (based on Coe & Yeung 2015; Yeung & Coe 2015)

Mode of strategic coupling	Tier	Ownership	Size	Specialization	Cost-capability ratio	Market development	Financial discipline
Organic	Lead firms and mega-suppliers	Mostly domestic, foreign firms may be lured by specialized localized knowledge	Large	Medium to high	Lead firms tend to command high capabilities based on specialized assets and know-how	Lead firms sustain brand recognition and shape the end markets	Frequently listed on a stock exchange, hence operating under the ‘shareholder value paradigm’
Functional mode (international partnership)	Higher tier suppliers	Mostly domestic	Medium and large	Medium to high	Higher tier suppliers command highly specialized assets and capabilities	Higher tier suppliers usually supply to a limited number of lead firms	Listing on a stock exchange is less frequent
Structural mode	Lower tier suppliers	Foreign (large firms), domestic (small, and medium-size firms)	Small, medium, and large	Low	Generic assets and weak capabilities, competing on costs	Lower tier suppliers deliver generic components often to multiple production networks	Rather exceptional listing on a stock exchange

Fig. 1. Number of employees and structure of the Czech electro-engineering companies according to ownership and tier in 2018 (based on data accessed from Merk.cz’s online database in July 2020)

Table 2. Typology of electrical engineering components used for classification of suppliers into tiers

Category	Electro-engineering industry
Lead firms – assembly	Nuclear/classic steam supply systems, turbine halls, hydro/wind/cogeneration power plant equipment, photovoltaic modules, transmission, and distribution grids
Tier 1 – design and production of devices/modules	Reactors, turbines, generators, boilers, solar cells, FV collectors, power transformers, energy storage
Tier 2 – production of components of modules and subsystems	Pumps, coolant systems and control elements, fuel rods, automation equipment, burners, stators, rotors, shafts, drives, yaw motors, blades, gearboxes, polysilicon wafers, distributors, electric meters, high voltage towers, safety and distribution transformers, batteries
Tier 3 – production of components of subsystems with relatively low value added	Distributors, cable bundles, current limiters, turbine paddles, valves Ventilation equipment, turbine paddles, high-pressure vessels, cable bundles, distributors, gates Polysilicon, connection boxes, Insulators copper wires, conductors, converters, circuit breakers, chokes, switchers, detectors, solar glass, tanks, metal parts and specialized armatures, and others

Fig. 2. Regional distribution of the Czech electro-engineering companies in 2018 (based on data accessed from Merk.cz’s online database in July 2020)

Table 3. The key absolute economic performance indicators according to firms’ ownership and position in the global production networks (GPNs) – mean and median values for the period 2012–2018 (thousand USD) (Source: data accessed from Merk.cz’s online database in July 2020)

Electro-engineering firms		Turnover		Gross profits		Added value		Number of firms
		Mean	Median	Mean	Median	Mean	Median
Lead firms	Domestic	10,709	8489	-798	108	1592	1444	2
	Mixed	55,581	53,838	-903	647	23,282	21,031	1
	Foreign	660,723	521,200	41,711	51,823	13,6641	155,961	4
	Total	388,556	180,258	23,478	5718	81,861	43,061	7
Tier 1	Domestic	35,407	30,493	2323	1112	12,164	8204	8
	Mixed	36,500	31,851	805	497	6551	6608	4
	Foreign	73,760	77,182	772	2650	25,257	22,650	3
	Total	43,369	37,890	1608	969	13,286	7858	15
Tier 2	Domestic	23,541	4942	71	201	5142	2751	16
	Mixed	11,157	7850	487	295	3449	2357	6
	Foreign	24,853	10,329	827	438	3487	4427	11
	Total	21,727	7938	399	340	4283	3165	33
Tier 3	Domestic	13,935	10,439	116	95	3195	2002	18
	Mixed	15,920	9969	41	205	3377	2034	12
	Foreign	59,590	30,923	1251	315	5129	7371	19
	Total	32,124	13,603	538	213	3989	3007	49
Firms outside GPNs	Domestic	6181	3477	472	137	2270	1027	23
Total	Domestic	16,035	6360	463	154	4366	1831	67
	Mixed	19,981	10,297	250	250	4813	2515	23
	Foreign	115,399	30,923	5460	461	20,490	7709	37
	Total	45,698	10,366	18,801	315	9144	2845	127

Table 4. The key relative economic performance indicators according to firms’ ownership and position in the global production networks (GPNs) – mean and median values for the period 2012–2018 (thousand USD) (Source: data accessed from Merk.cz’s online database in July 2020)

Electro-engineering firms		Profit-turnover ratio (in %)		Personnel costs-turnover ratio		Added value per employee (thousand USD**)		Wages per employee (thousand USD)		Number of firms
		Mean*	Median	Mean	Median	Mean	Median	Mean	Median
Lead firms	domestic	-1.18	-.01	1.04	.16	33.94	34.91	26.07	31.01	2
	mixed	-.01	.03	.32	.33	34.04	30.75	26.12	27.34	1
	foreign	.09	.07	.16	.15	48.14	43.49	30.31	30.71	4
	totals	-.28	.05	.43	.16	42.07	37.05	28.50	31.00	7
Tier 1	domestic	.07	.06	.23	.24	33.93	38.87	30.78	28.15	8
	mixed	.05	.01	.17	.15	55.51	41.55	36.84	34.36	4
	foreign	.00	.02	.29	.28	23.07	29.32	23.09	24.84	3
	totals	.05	.03	.23	.22	37.51	36.61	30.86	27.88	15
Tier 2	domestic	.04	.04	.31	.27	39.24	26.59	24.61	19.98	16
	mixed	.07	.04	.24	.23	37.08	18.98	28.05	15.88	6
	foreign	.06	.06	.29	.26	21.35	30.81	26.94	22.74	11
	totals	.05	.04	.29	.26	32.88	28.73	26.01	20.36	33
Tier 3	domestic	.02	.02	.27	.25	26.91	25.26	21.28	20.15	18
	mixed	.00	.04	.23	.24	11.19	26.26	26.30	21.60	12
	foreign	.04	.03	.18	.17	-11.54	27.58	26.49	22.36	19
	totals	.02	.03	.23	.21	8.15	26.11	24.53	21.56	49
Firms outside GPNs	domestic	.12	.06	.51	.27	33.00	27.45	26.59	21.83	23
Totals	domestic	.03	.04	.38	.26	32.99	27.22	25.17	21.83	67
	mixed	.03	.03	.23	.22	26.64	30.57	28.58	23.23	23
	foreign	.05	.03	.22	.18	7.50	30.44	26.76	22.79	37
	totals	.03	.04	.31	.24	24.41	29.49	26.25	22.65	127

Notes: *Mean and median values are weighted by the average number of employees in years 2017 and 2018; **Average exchange rate in 2018 was 1 USD = 21.7 CZK (Source: Czech National Bank web pages, July 2020)

Table 5. The relationships between economic performance indicators and ownership, tier, and size, tested by Kruskal-Wallis tests (N = 127 (Source: data accessed from Merk.cz’s online database in July 2018)

Table 6. Correlation analysis of the relationships between the firms’ characteristics and their economic performance for the period 2012–2018 (N = 127)

Correlations
Spearman’s rho		Tier***	Owner-ship	Firm's size	Supply outside the Electro-engineering industry	Supply within the Electro-engineering industry	Profit-turnover ratio	Personnel costs-turnover ratio	Added value per employment	Wages per employment	Regional GDP	Regions****	Stocks
Tier	Corr. Coef.	1.000	.246**	.236**	-.070	-.001	.012	-.108	.202*	.205*	.143	-.065	.247**
	Sig. (2-tailed)	.	.005	.008	.437	.988	.897	.229	.023	.021	.109	.467	.005
Ownership	Corr. Coef.	.246**	1.000	.375**	.070	-.052	-.027	-.265**	.071	.091	.002	-.057	.362**
	Sig. (2-tailed)	.005	.	.000	.433	.563	.763	.003	.431	.310	.981	.523	.000
Firm’s size	Corr. Coef.	.236**	.375**	1.000	.219*	.214*	.029	-.079	.074	.023	.047	.021	.440**
	Sig. (2-tailed)	.008	.000	.	.013	.016	.745	.377	.411	.800	.596	.819	.000
Supplies outside the sector	Corr. Coef.	-.070	.070	.219*	1.000	.263**	-.002	-.144	.071	.044	.075	.010	.173
	Sig. (2-tailed)	.437	.433	.013	.	.003	.986	.107	.429	.620	.402	.915	.052
Supplies within the sector	Corr. Coef.	-.001	-.052	.214*	.263**	1.000	-.064	-.023	.032	.048	.019	.214*	-.039
	Sig. (2-tailed)	.988	.563	.016	.003	.	.475	.798	.721	.593	.833	.016	.663
Profit-turnover ratio	Corr. Coef.	.012	-.027	.029	-.002	-.064	1.000	.166	.420**	.106	.166	-.129	.039
	Sig. (2-tailed)	.897	.763	.745	.986	.475	.	.062	.000	.234	.062	.149	.665
Personnel costs-turnover ratio	Corr. Coef.	-.108	-.265**	-.079	-.144	-.023	.166	1.000	-.173	-.266**	-.046	.056	-.240**
	Sig. (2-tailed)	.229	.003	.377	.107	.798	.062	.	.052	.002	.611	.529	.007
Added value per employee	Corr. Coef.	.202*	.071	.074	.071	.032	.420**	-.173	1.000	.781**	.272**	-.219*	.239**
	Sig. (2-tailed)	.023	.431	.411	.429	.721	.000	.052	.	.000	.002	.013	.007
Wages per employee	Corr. Coef.	.205*	.091	.023	.044	.048	.106	-.266**	.781**	1.000	.303**	-.223*	.239**
	Sig. (2-tailed)	.021	.310	.800	.620	.593	.234	.002	.000	.	.001	.012	.007
Regional GDP (gross domestic product)	Corr. Coef.	.143	.002	.047	.075	.019	.074	-.046	.272**	.303**	1.000	-.270**	.063
	Sig. (2-tailed)	.109	.981	.596	.402	.833	.411	.611	.002	.001	.	.002	.481
Regions	Corr. Coef.	-.065	-.057	.021	.010	.214*	-.129	.056	-.219*	-.223*	-.270**	1.000	-.188*
	Sig. (2-tailed)	.467	.523	.819	.915	.016	.149	.529	.013	.012	.002	.	.035
Stock market	Corr. Coef.	.247**	.362**	.440**	.173	-.039	.039	-.240**	.239**	.239**	.063	-.188*	1.000
	Sig. (2-tailed)	.005	.000	.000	.052	.663	.665	.007	.007	.007	.481	.035	.

Notes: *Correlation is significant at the 0.05 level (2-tailed); ** Correlation significant at the 0.01 level (2-tailed); ***Coding of position in global production networks (GPNs): the higher the value, the higher the position in GPNs, i.e. 4 – lead firm, 3 – tier 1 supplier, 2 – tier 2 supplier, and so forth. Similarly, other categories are coded, except the level of specialization, where 1 – dominant, 2 – significant, and 3 – partial involvement in electro-engineering industry; ****The variable ‘Regions’ is ordinal and includes 14 Czech NUTS 3 level regions

Fig. 5. Relationship between profit-turnover ratio and wages per employee for the period 2012–2018 based on data accessed from Merk.cz’s online database in July 2020)

Fig. 3. Regional distribution of firms’ profit-turnover ratio, median values for the period 2012–2018 (in %) (based on data accessed from Merk.cz’s online database in July 2020)

Fig. 4. Regional distribution of firms’ wages per employee, median values for the period 2012–2018 (thousand USD) (based on data accessed from Merk.cz’s online database in July 2020)

Coe, N.M & Yeung, H.W.-C. 2015. Global Production Networks: Theorizing Economic Development in an Interconnected World. Oxford: Oxford University Press.

 Google Scholar

Yeung, H.W.-C. & Coe, N.M. 2015. Toward a dynamic theory of global production networks. Economic Geography 91, 29–58.

 Web of Science ®Google Scholar